d0b3cb4fa7
### What this PR does / why we need it? Eliminate redundant operations in the code to improve performance ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? CI passed --------- Signed-off-by: Yaphets24 <d_mym0618@163.com> Signed-off-by: MengqingCao <cmq0113@163.com> Co-authored-by: MengqingCao <cmq0113@163.com>
93 lines
2.9 KiB
Python
93 lines
2.9 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from typing import Optional, Tuple
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import torch
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from vllm.model_executor.layers.rotary_embedding import (
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DeepseekScalingRotaryEmbedding, RotaryEmbedding)
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def rope_forward_oot(
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self,
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor,
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offsets: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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import torch_npu
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if self.cos_sin_cache.device != query.device:
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self.cos_sin_cache = self.cos_sin_cache.to(query.device)
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if self.cos_sin_cache.dtype != query.dtype:
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self.cos_sin_cache = self.cos_sin_cache.to(query.dtype)
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if offsets is not None:
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raise NotImplementedError(
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"Batched rotary embedding is currently not supported on NPU.")
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else:
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# TODO: Remove the contiguous in the future.
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query = query.contiguous()
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key = key.contiguous()
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torch_npu.npu_rope(
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positions,
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query,
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key,
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self.head_size,
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self.cos_sin_cache,
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self.is_neox_style,
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)
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return query, key
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def rope_deepseek_forward_oot(
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self,
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor,
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offsets: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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import torch_npu
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if self.cos_sin_cache.device != query.device:
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self.cos_sin_cache = self.cos_sin_cache.to(query.device)
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if self.cos_sin_cache.dtype != query.dtype:
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self.cos_sin_cache = self.cos_sin_cache.to(query.dtype)
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if offsets is not None:
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raise NotImplementedError(
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"Batched rotary embedding is currently not supported on NPU.")
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else:
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# TODO: Remove the contiguous in the future.
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ori_query_shape, ori_key_shape = query.shape, key.shape
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query = query.contiguous().view(query.shape[0], -1)
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key = key.contiguous().view(query.shape[0], -1)
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torch_npu.npu_rope(
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positions,
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query,
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key,
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self.head_size,
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self.cos_sin_cache,
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self.is_neox_style,
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)
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query = query.view(ori_query_shape)
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key = key.view(ori_key_shape)
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return query, key
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RotaryEmbedding.forward_oot = rope_forward_oot
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DeepseekScalingRotaryEmbedding.forward = rope_deepseek_forward_oot
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